Fuzzy risk stratification and risk assessment model for clinical monitoring in the ICU

The decisions that clinicians make in intensive care units (ICUs) based on monitored parameters reflecting physiological deterioration are of major medical and biomedical engineering interest. These parameters have been investigated and assessed for their usefulness in risk assessment.

Totally, 127 ICU adult patients were studied. They were selected from a MIMIC II Waveform Database Matched Subset and had continuous monitoring of heart rate, invasive blood pressure, and oxygen saturation. The monitored data were dimension reduced using deep learning autoencoders and then used to train a support vector machine model (SVM). A combination of methods including fuzzy c-means clustering (FCM), and a random forest (RF) was used to determine the risk levels.

When classifying patients into stable or deteriorating groups the main performance parameter was the receiver operating characteristics (ROC). The area under the ROC (AUROC) was 93.2 (95% CI (92.9-93.4)) with sensitivity and specificity values of 0.80 and 0.89, respectively. The suggested fuzzy risk levels using the combined method of the FCM clustering and RF achieved an accuracy of 1 (0.9999, 1), with both sensitivity and specificity values equal to 1.

The potential for using models in risk assessment to estimate a patient's physiological status, stable or deteriorating, within 4 h has been demonstrated. The study was based on retrospective analysis and further studies are needed to evaluate the impact on clinical outcomes using this model.